The document discusses different types of data visualization including explanatory visualization which is used to transmit a point of view and exploratory visualization which is used to discover patterns in a dataset. It provides examples of different visualization techniques such as infographics, pivot tables, network structures, and trends. The document also discusses the concept of "literate visualization" which combines writing and diagrams.

1. Data Visualisation: A Taster
Tony Hirst Martin Hawksey
Dept of Communication and Systems, JISC CETIS
The Open University
@psychemedia/blog.ouseful.info @mhawksey/mashe.hawksey.info

2. <A QUICK NOTE>

3. “The most interesting
visualisationsof your data
will be produced by
someone else”

4. Presentation Graphics
vs.
Visual Analysis

5. Explanatory visualization
Data visualizations that are used to
transmit information or a point of
view from the designer to the
reader. Explanatory visualizations
typically have a specific “story” or
information that they are intended
to transmit.
Exploratory visualization
Data visualizations that are used by
the designer for self-informative
purposes to discover
patterns, trends, or sub-problems
in a dataset. Exploratory
visualizations typically don’t have
an already-known story.

6. Data sketches
[ Amanda Cox, New York Times ]

7. Infographics
≠
(Exploratory) Visualisation

12. Macroscopes

15. Expressions of
Structure

18. Hierarchical data and treemaps - medals
Pivot tables

19. O’Reilly Annual Review of Book Sales

20. Network structure
Node and edges
All nodes the same sort of thing
Edges may be directed or undirected
Edges may be weighted
Bipartite graph – two sorts of nodes
Can collapse a bipartite graph to
get a new view over the data

24. Dynamics

26. Trends
Autocorrelation

27. @mediaczar
(Accession Plot)

32. “Literate visualisation”
(writing diagrams)

35. ggplot( mydata,
aes(x=xVal,y=yVal)) +
geom_point() +
facet_wrap(~mygroup)

36. Data Application Output
Data [Code] Output